Recently, in order to provide global service and multimedia communication service, early introduction has been planned for the next-generation portable telephones. These next-generation telephone employ IMT-2000 (International Mobile Telecommunications 2000, a standard by the International Telecommunication Union), the next-generation mobile communication system. For a next-generation portable telephone supporting IMT-2000, a maximum bandwidth of 2 Million bits per second (Mbps) is provided, and the provision of a video distribution service is also planned as an application. However, using a portable telephone to view video for an extended period of time is difficult for a variety of reasons, including device limitations, such as device sizes and resolutions, and communication fees.
Therefore, a system is required whereby a content digest, representative of the enormous amount of video data required for the presentation of the digest, is needed. For example, according to MPEG-7 (the seventh version of a standard created by the Motion Pictures Expert Group), in order for a high-speed search engine to be used for multimedia data, the standardization of meta data must have progressed to the point that descriptive audio/visual data specifications can be used as search keywords.
The simplest system for generating a video digest using meta data is to employ a method whereby, before the video digest is prepared, a search is performed in a period wherein meta data pertinent to query data are present. However, since such a system can perform only a binary operation for attesting to the presence of pertinent meta data, no priority can be assigned to the search results, even when multiple data set queries are processed.
According to another system for preparing a rule appropriate for a video domain and for calculating an importance level in accordance with rules for the generation of a video digest (see “Digest Audio System for a TV Reception Terminal,” Transaction of Information Processing Society of Japan, Vol. 41, No. SIG3 (TOD 6), the disclosure of which is hereby incorporated by reference), an additional technique is available for preparing a video digest meta data. This technique employs a similarity between a user profile and a characteristic of the “tf.idf” method for weighting words. For a description of the tf.idf, see “Automatic Construction of Personalized TV News Programs,” Association of Computing Machinery (ACM) Multimedia Conf., 323-331 (1999), the disclosure of which is hereby incorporated by reference.
However, according to the above technique for calculating a rule-based importance level and preparing a video digest, a personally prepared importance level calculation rule must be devised in advance and used for calculating an importance level for structured meta data using tags. Thus, a load is imposed on a user for the provision of structured meta data and for the generation of importance level calculation rules for each domain. In addition, according to the method employed for preparing a video digest using the similarity to a user profile, results cannot be obtained unless a satisfactory amount of meta data is available.
Furthermore, since currently a content provider must manually prepare all video digests, a great deal of labor is required, and generating digests appropriate to the demands of a variety of audiences is difficult. Even when progress in content standardization has been achieved, not all contents include meta data wherein a scene is described in detail. Moreover, a described scene is not always represented by a closed caption that displays subtitle data or text that has been obtained using speech recognition.
Specifically, using video as an example, a video digest technique is required to enable users to efficiently view and listen to the enormous amount of video content that is available. However, at the present, there is no alternative to viewing and listening to material prepared by content providers, and video digest content representative of individual preferences is not available for those users whose preferences differ. In order to prepare digests consonant with individual preferences, there is a method that can be used that involves the gathering of data covering individual tastes and then constructing, from this data, detailed user profiles that can be employed to prepare optimal video digests for individual users. However, since generally much time and labor are required to acquire the data for even one user profile, constructing detailed profiles for all users is not practical because of the huge work load involved.
Thus, there is a need to overcome the obstacles of a large work load for constructing detailed profiles for each user and for providing content digests, yet provide each user with some type of profile and a content digest tailored to the user.